





import torch
from torch import nn



class 					__snake_case	(  nn.Module    ):


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_=1   ,				A_=False     ):





        '''simple docstring'''

        super().__init__()

        SCREAMING_SNAKE_CASE__											=			n_token
        SCREAMING_SNAKE_CASE__											=			d_embed
        SCREAMING_SNAKE_CASE__											=			d_proj

        SCREAMING_SNAKE_CASE__											=			cutoffs + [n_token]
        SCREAMING_SNAKE_CASE__											=			[0] + self.cutoffs
        SCREAMING_SNAKE_CASE__											=			div_val

        SCREAMING_SNAKE_CASE__											=			self.cutoffs[0]
        SCREAMING_SNAKE_CASE__											=			len(self.cutoffs     ) - 1
        SCREAMING_SNAKE_CASE__											=			self.shortlist_size + self.n_clusters

        if self.n_clusters > 0:
            SCREAMING_SNAKE_CASE__											=			nn.Parameter(torch.zeros(self.n_clusters   ,				self.d_embed     )     )
            SCREAMING_SNAKE_CASE__											=			nn.Parameter(torch.zeros(self.n_clusters     )     )

        SCREAMING_SNAKE_CASE__											=			nn.ModuleList()
        SCREAMING_SNAKE_CASE__											=			nn.ParameterList()

        if div_val == 1:
            for i in range(len(self.cutoffs     )     ):
                if d_proj != d_embed:
                    self.out_projs.append(nn.Parameter(torch.FloatTensor(A_   ,				A_     )     )     )
                else:
                    self.out_projs.append(A_     )

            self.out_layers.append(nn.Linear(A_   ,				A_     )     )
        else:
            for i in range(len(self.cutoffs     )     ):
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.cutoff_ends[i], self.cutoff_ends[i + 1]
                SCREAMING_SNAKE_CASE__											=			d_embed // (div_val**i)

                self.out_projs.append(nn.Parameter(torch.FloatTensor(A_   ,				A_     )     )     )

                self.out_layers.append(nn.Linear(A_   ,				r_idx - l_idx     )     )

        SCREAMING_SNAKE_CASE__											=			keep_order



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        if proj is None:
            SCREAMING_SNAKE_CASE__											=			nn.functional.linear(A_   ,				A_   ,				bias=A_     )
        else:
            # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
            SCREAMING_SNAKE_CASE__											=			nn.functional.linear(A_   ,				proj.t().contiguous()     )
            SCREAMING_SNAKE_CASE__											=			nn.functional.linear(A_   ,				A_   ,				bias=A_     )
            # else:
            #     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
            #     if bias is not None:
            #         logit = logit + bias

        return logit



    def  lowercase_					(       self   ,				A_   ,				A_=None   ,				A_=False     ):





        '''simple docstring'''

        if labels is not None:
            # Shift so that tokens < n predict n
            SCREAMING_SNAKE_CASE__											=			hidden[..., :-1, :].contiguous()
            SCREAMING_SNAKE_CASE__											=			labels[..., 1:].contiguous()
            SCREAMING_SNAKE_CASE__											=			hidden.view(-1   ,				hidden.size(-1     )     )
            SCREAMING_SNAKE_CASE__											=			labels.view(-1     )
            if hidden.size(0     ) != labels.size(0     ):
                raise RuntimeError('''Input and labels should have the same size in the batch dimension.'''     )
        else:
            SCREAMING_SNAKE_CASE__											=			hidden.view(-1   ,				hidden.size(-1     )     )

        if self.n_clusters == 0:
            SCREAMING_SNAKE_CASE__											=			self._compute_logit(A_   ,				self.out_layers[0].weight   ,				self.out_layers[0].bias   ,				self.out_projs[0]     )
            if labels is not None:
                SCREAMING_SNAKE_CASE__											=			labels != -1_00
                SCREAMING_SNAKE_CASE__											=			torch.zeros_like(A_   ,				dtype=hidden.dtype   ,				device=hidden.device     )
                SCREAMING_SNAKE_CASE__											=			(
                    -nn.functional.log_softmax(A_   ,				dim=-1     )[mask].gather(1   ,				labels[mask].unsqueeze(1     )     ).squeeze(1     )
                )
            else:
                SCREAMING_SNAKE_CASE__											=			nn.functional.log_softmax(A_   ,				dim=-1     )
        else:
            # construct weights and biases
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			[], []
            for i in range(len(self.cutoffs     )     ):
                if self.div_val == 1:
                    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.cutoff_ends[i], self.cutoff_ends[i + 1]
                    SCREAMING_SNAKE_CASE__											=			self.out_layers[0].weight[l_idx:r_idx]
                    SCREAMING_SNAKE_CASE__											=			self.out_layers[0].bias[l_idx:r_idx]
                else:
                    SCREAMING_SNAKE_CASE__											=			self.out_layers[i].weight
                    SCREAMING_SNAKE_CASE__											=			self.out_layers[i].bias

                if i == 0:
                    SCREAMING_SNAKE_CASE__											=			torch.cat([weight_i, self.cluster_weight]   ,				dim=0     )
                    SCREAMING_SNAKE_CASE__											=			torch.cat([bias_i, self.cluster_bias]   ,				dim=0     )

                weights.append(A_     )
                biases.append(A_     )

            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			weights[0], biases[0], self.out_projs[0]

            SCREAMING_SNAKE_CASE__											=			self._compute_logit(A_   ,				A_   ,				A_   ,				A_     )
            SCREAMING_SNAKE_CASE__											=			nn.functional.log_softmax(A_   ,				dim=1     )

            if labels is None:
                SCREAMING_SNAKE_CASE__											=			hidden.new_empty((head_logit.size(0     ), self.n_token)     )
            else:
                SCREAMING_SNAKE_CASE__											=			torch.zeros_like(A_   ,				dtype=hidden.dtype   ,				device=hidden.device     )

            SCREAMING_SNAKE_CASE__											=			0
            SCREAMING_SNAKE_CASE__											=			[0] + self.cutoffs
            for i in range(len(A_     ) - 1     ):
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			cutoff_values[i], cutoff_values[i + 1]

                if labels is not None:
                    SCREAMING_SNAKE_CASE__											=			(labels >= l_idx) & (labels < r_idx)
                    SCREAMING_SNAKE_CASE__											=			mask_i.nonzero().squeeze()

                    if indices_i.numel() == 0:
                        continue

                    SCREAMING_SNAKE_CASE__											=			labels.index_select(0   ,				A_     ) - l_idx
                    SCREAMING_SNAKE_CASE__											=			head_logprob.index_select(0   ,				A_     )
                    SCREAMING_SNAKE_CASE__											=			hidden.index_select(0   ,				A_     )
                else:
                    SCREAMING_SNAKE_CASE__											=			hidden

                if i == 0:
                    if labels is not None:
                        SCREAMING_SNAKE_CASE__											=			head_logprob_i.gather(1   ,				target_i[:, None]     ).squeeze(1     )
                    else:
                        SCREAMING_SNAKE_CASE__											=			head_logprob[:, : self.cutoffs[0]]
                else:
                    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			weights[i], biases[i], self.out_projs[i]

                    SCREAMING_SNAKE_CASE__											=			self._compute_logit(A_   ,				A_   ,				A_   ,				A_     )
                    SCREAMING_SNAKE_CASE__											=			nn.functional.log_softmax(A_   ,				dim=1     )
                    SCREAMING_SNAKE_CASE__											=			self.cutoffs[0] + i - 1  # No probability for the head cluster
                    if labels is not None:
                        SCREAMING_SNAKE_CASE__											=			head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
                            1   ,				target_i[:, None]     ).squeeze(1     )
                    else:
                        SCREAMING_SNAKE_CASE__											=			head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
                        SCREAMING_SNAKE_CASE__											=			logprob_i

                if labels is not None:
                    if (hasattr(self   ,				'''keep_order'''     ) and self.keep_order) or keep_order:
                        out.index_copy_(0   ,				A_   ,				-logprob_i     )
                    else:
                        out[offset : offset + logprob_i.size(0     )].copy_(-logprob_i     )
                    offset += logprob_i.size(0     )

        return out




    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        if self.n_clusters == 0:
            SCREAMING_SNAKE_CASE__											=			self._compute_logit(A_   ,				self.out_layers[0].weight   ,				self.out_layers[0].bias   ,				self.out_projs[0]     )
            return nn.functional.log_softmax(A_   ,				dim=-1     )
        else:
            # construct weights and biases
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			[], []
            for i in range(len(self.cutoffs     )     ):
                if self.div_val == 1:
                    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.cutoff_ends[i], self.cutoff_ends[i + 1]
                    SCREAMING_SNAKE_CASE__											=			self.out_layers[0].weight[l_idx:r_idx]
                    SCREAMING_SNAKE_CASE__											=			self.out_layers[0].bias[l_idx:r_idx]
                else:
                    SCREAMING_SNAKE_CASE__											=			self.out_layers[i].weight
                    SCREAMING_SNAKE_CASE__											=			self.out_layers[i].bias

                if i == 0:
                    SCREAMING_SNAKE_CASE__											=			torch.cat([weight_i, self.cluster_weight]   ,				dim=0     )
                    SCREAMING_SNAKE_CASE__											=			torch.cat([bias_i, self.cluster_bias]   ,				dim=0     )

                weights.append(A_     )
                biases.append(A_     )

            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			weights[0], biases[0], self.out_projs[0]
            SCREAMING_SNAKE_CASE__											=			self._compute_logit(A_   ,				A_   ,				A_   ,				A_     )

            SCREAMING_SNAKE_CASE__											=			hidden.new_empty((head_logit.size(0     ), self.n_token)     )
            SCREAMING_SNAKE_CASE__											=			nn.functional.log_softmax(A_   ,				dim=1     )

            SCREAMING_SNAKE_CASE__											=			[0] + self.cutoffs
            for i in range(len(A_     ) - 1     ):
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			cutoff_values[i], cutoff_values[i + 1]

                if i == 0:
                    SCREAMING_SNAKE_CASE__											=			head_logprob[:, : self.cutoffs[0]]
                else:
                    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			weights[i], biases[i], self.out_projs[i]

                    SCREAMING_SNAKE_CASE__											=			self._compute_logit(A_   ,				A_   ,				A_   ,				A_     )
                    SCREAMING_SNAKE_CASE__											=			nn.functional.log_softmax(A_   ,				dim=1     )

                    SCREAMING_SNAKE_CASE__											=			head_logprob[:, -i] + tail_logprob_i
                    SCREAMING_SNAKE_CASE__											=			logprob_i

            return out






'''simple docstring'''


import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
UpperCAmelCase_					=   {"LayoutLMv2Config", "LayoutLMv3Config"}





@is_pipeline_test
class  __lowercase    (	unittest.TestCase			):
   _a    						=   MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
   _a    						=   TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

   if model_mapping is not None:
      _a    						=   {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
   if tf_model_mapping is not None:
      _a    						=   {
          config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
      }







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Optional[Any]:
     __a								=       ZeroShotClassificationPipeline(
         model=UpperCamelCase   ,				tokenizer=UpperCamelCase   ,				candidate_labels=['polics', 'health']   )
     return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Tuple:
     __a								=       classifier('Who are you voting for in 2020?'   ,				candidate_labels='politics'   )
     self.assertEqual(UpperCamelCase   ,				{'sequence': ANY(UpperCamelCase   ), 'labels': [ANY(UpperCamelCase   )], 'scores': [ANY(UpperCamelCase   )]}   )

     # No kwarg
     __a								=       classifier('Who are you voting for in 2020?'   ,				['politics']   )
     self.assertEqual(UpperCamelCase   ,				{'sequence': ANY(UpperCamelCase   ), 'labels': [ANY(UpperCamelCase   )], 'scores': [ANY(UpperCamelCase   )]}   )

     __a								=       classifier('Who are you voting for in 2020?'   ,				candidate_labels=['politics']   )
     self.assertEqual(UpperCamelCase   ,				{'sequence': ANY(UpperCamelCase   ), 'labels': [ANY(UpperCamelCase   )], 'scores': [ANY(UpperCamelCase   )]}   )

     __a								=       classifier('Who are you voting for in 2020?'   ,				candidate_labels='politics, public health'   )
     self.assertEqual(
         UpperCamelCase   ,				{'sequence': ANY(UpperCamelCase   ), 'labels': [ANY(UpperCamelCase   ), ANY(UpperCamelCase   )], 'scores': [ANY(UpperCamelCase   ), ANY(UpperCamelCase   )]}   )
     self.assertAlmostEqual(sum(nested_simplify(outputs['scores']   )   )   ,				1.0   )

     __a								=       classifier('Who are you voting for in 2020?'   ,				candidate_labels=['politics', 'public health']   )
     self.assertEqual(
         UpperCamelCase   ,				{'sequence': ANY(UpperCamelCase   ), 'labels': [ANY(UpperCamelCase   ), ANY(UpperCamelCase   )], 'scores': [ANY(UpperCamelCase   ), ANY(UpperCamelCase   )]}   )
     self.assertAlmostEqual(sum(nested_simplify(outputs['scores']   )   )   ,				1.0   )

     __a								=       classifier(
         'Who are you voting for in 2020?'   ,				candidate_labels='politics'   ,				hypothesis_template='This text is about {}'   )
     self.assertEqual(UpperCamelCase   ,				{'sequence': ANY(UpperCamelCase   ), 'labels': [ANY(UpperCamelCase   )], 'scores': [ANY(UpperCamelCase   )]}   )

     # https://github.com/huggingface/transformers/issues/13846
     __a								=       classifier(['I am happy']   ,				['positive', 'negative']   )
     self.assertEqual(
         UpperCamelCase   ,				[
             {'sequence': ANY(UpperCamelCase   ), 'labels': [ANY(UpperCamelCase   ), ANY(UpperCamelCase   )], 'scores': [ANY(UpperCamelCase   ), ANY(UpperCamelCase   )]}
             for i in range(1   )
         ]   ,				)
     __a								=       classifier(['I am happy', 'I am sad']   ,				['positive', 'negative']   )
     self.assertEqual(
         UpperCamelCase   ,				[
             {'sequence': ANY(UpperCamelCase   ), 'labels': [ANY(UpperCamelCase   ), ANY(UpperCamelCase   )], 'scores': [ANY(UpperCamelCase   ), ANY(UpperCamelCase   )]}
             for i in range(2   )
         ]   ,				)

     with self.assertRaises(UpperCamelCase   ):
       classifier(''   ,				candidate_labels='politics'   )

     with self.assertRaises(UpperCamelCase   ):
       classifier(UpperCamelCase   ,				candidate_labels='politics'   )

     with self.assertRaises(UpperCamelCase   ):
       classifier('Who are you voting for in 2020?'   ,				candidate_labels=''   )

     with self.assertRaises(UpperCamelCase   ):
       classifier('Who are you voting for in 2020?'   ,				candidate_labels=UpperCamelCase   )

     with self.assertRaises(UpperCamelCase   ):
       classifier(
           'Who are you voting for in 2020?'   ,				candidate_labels='politics'   ,				hypothesis_template='Not formatting template'   ,				)

     with self.assertRaises(UpperCamelCase   ):
       classifier(
           'Who are you voting for in 2020?'   ,				candidate_labels='politics'   ,				hypothesis_template=UpperCamelCase   ,				)

     self.run_entailment_id(UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Optional[int]:
     __a								=       zero_shot_classifier.model.config
     __a								=       config.labelaid
     __a								=       zero_shot_classifier.entailment_id

     __a								=       {'LABEL_0': 0, 'LABEL_1': 1, 'LABEL_2': 2}
     self.assertEqual(zero_shot_classifier.entailment_id   ,				-1   )

     __a								=       {'entailment': 0, 'neutral': 1, 'contradiction': 2}
     self.assertEqual(zero_shot_classifier.entailment_id   ,				0   )

     __a								=       {'ENTAIL': 0, 'NON-ENTAIL': 1}
     self.assertEqual(zero_shot_classifier.entailment_id   ,				0   )

     __a								=       {'ENTAIL': 2, 'NEUTRAL': 1, 'CONTR': 0}
     self.assertEqual(zero_shot_classifier.entailment_id   ,				2   )

     __a								=       original_labelaid
     self.assertEqual(UpperCamelCase   ,				zero_shot_classifier.entailment_id   )







   @require_torch
   def 		UpperCamelCase__      (						self   )					->     Tuple:
     __a								=       pipeline(
         'zero-shot-classification'   ,				model='sshleifer/tiny-distilbert-base-cased-distilled-squad'   ,				framework='pt'   ,				)
     # There was a regression in 4.10 for this
     # Adding a test so we don't make the mistake again.
     # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
     zero_shot_classifier(
         'Who are you voting for in 2020?' * 100   ,				candidate_labels=['politics', 'public health', 'science']   )







   @require_torch
   def 		UpperCamelCase__      (						self   )					->     List[str]:
     __a								=       pipeline(
         'zero-shot-classification'   ,				model='sshleifer/tiny-distilbert-base-cased-distilled-squad'   ,				framework='pt'   ,				)
     __a								=       zero_shot_classifier(
         'Who are you voting for in 2020?'   ,				candidate_labels=['politics', 'public health', 'science']   )

     self.assertEqual(
         nested_simplify(UpperCamelCase   )   ,				{
             'sequence': 'Who are you voting for in 2020?',
             'labels': ['science', 'public health', 'politics'],
             'scores': [0.333, 0.333, 0.333],
         }   ,				)







   @require_tf
   def 		UpperCamelCase__      (						self   )					->     Dict:
     __a								=       pipeline(
         'zero-shot-classification'   ,				model='sshleifer/tiny-distilbert-base-cased-distilled-squad'   ,				framework='tf'   ,				)
     __a								=       zero_shot_classifier(
         'Who are you voting for in 2020?'   ,				candidate_labels=['politics', 'public health', 'science']   )

     self.assertEqual(
         nested_simplify(UpperCamelCase   )   ,				{
             'sequence': 'Who are you voting for in 2020?',
             'labels': ['science', 'public health', 'politics'],
             'scores': [0.333, 0.333, 0.333],
         }   ,				)







   @slow
   @require_torch
   def 		UpperCamelCase__      (						self   )					->     List[str]:
     __a								=       pipeline('zero-shot-classification'   ,				model='roberta-large-mnli'   ,				framework='pt'   )
     __a								=       zero_shot_classifier(
         'Who are you voting for in 2020?'   ,				candidate_labels=['politics', 'public health', 'science']   )

     self.assertEqual(
         nested_simplify(UpperCamelCase   )   ,				{
             'sequence': 'Who are you voting for in 2020?',
             'labels': ['politics', 'public health', 'science'],
             'scores': [0.976, 0.015, 0.009],
         }   ,				)
     __a								=       zero_shot_classifier(
         'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
         ' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
         ' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
         ' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
         ' machine translation tasks show these models to be superior in quality while being more parallelizable'
         ' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
         ' English-to-German translation task, improving over the existing best results, including ensembles by'
         ' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
         ' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
         ' fraction of the training costs of the best models from the literature. We show that the Transformer'
         ' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
         ' large and limited training data.'   ,				candidate_labels=['machine learning', 'statistics', 'translation', 'vision']   ,				multi_label=UpperCamelCase   ,				)
     self.assertEqual(
         nested_simplify(UpperCamelCase   )   ,				{
             'sequence': (
                 'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
                 ' networks in an encoder-decoder configuration. The best performing models also connect the'
                 ' encoder and decoder through an attention mechanism. We propose a new simple network'
                 ' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
                 ' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
                 ' superior in quality while being more parallelizable and requiring significantly less time to'
                 ' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
                 ' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
                 ' English-to-French translation task, our model establishes a new single-model state-of-the-art'
                 ' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
                 ' costs of the best models from the literature. We show that the Transformer generalizes well to'
                 ' other tasks by applying it successfully to English constituency parsing both with large and'
                 ' limited training data.'
             ),
             'labels': ['translation', 'machine learning', 'vision', 'statistics'],
             'scores': [0.817, 0.713, 0.018, 0.018],
         }   ,				)







   @slow
   @require_tf
   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a								=       pipeline('zero-shot-classification'   ,				model='roberta-large-mnli'   ,				framework='tf'   )
     __a								=       zero_shot_classifier(
         'Who are you voting for in 2020?'   ,				candidate_labels=['politics', 'public health', 'science']   )

     self.assertEqual(
         nested_simplify(UpperCamelCase   )   ,				{
             'sequence': 'Who are you voting for in 2020?',
             'labels': ['politics', 'public health', 'science'],
             'scores': [0.976, 0.015, 0.009],
         }   ,				)
     __a								=       zero_shot_classifier(
         'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
         ' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
         ' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
         ' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
         ' machine translation tasks show these models to be superior in quality while being more parallelizable'
         ' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
         ' English-to-German translation task, improving over the existing best results, including ensembles by'
         ' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
         ' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
         ' fraction of the training costs of the best models from the literature. We show that the Transformer'
         ' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
         ' large and limited training data.'   ,				candidate_labels=['machine learning', 'statistics', 'translation', 'vision']   ,				multi_label=UpperCamelCase   ,				)
     self.assertEqual(
         nested_simplify(UpperCamelCase   )   ,				{
             'sequence': (
                 'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
                 ' networks in an encoder-decoder configuration. The best performing models also connect the'
                 ' encoder and decoder through an attention mechanism. We propose a new simple network'
                 ' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
                 ' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
                 ' superior in quality while being more parallelizable and requiring significantly less time to'
                 ' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
                 ' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
                 ' English-to-French translation task, our model establishes a new single-model state-of-the-art'
                 ' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
                 ' costs of the best models from the literature. We show that the Transformer generalizes well to'
                 ' other tasks by applying it successfully to English constituency parsing both with large and'
                 ' limited training data.'
             ),
             'labels': ['translation', 'machine learning', 'vision', 'statistics'],
             'scores': [0.817, 0.713, 0.018, 0.018],
         }   ,				)



